{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hyyN-2qyK_T2"
   },
   "source": [
    "# Train your Second Agent\n",
    "\n",
    "In this Notebook we will explore yet another environment, train the agent and then share the result using Huggingface ecosystem.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qqvt5EqiOqmS"
   },
   "source": [
    "#### Running in Colab/Kaggle\n",
    "\n",
    "If you are running this on Colab or Kaggle, please uncomment below cell and run this to install required dependencies.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## uncomment and execute this cell to install all the the dependencies if running in Google Colab or Kaggle\n",
    "\n",
    "# !apt-get update \n",
    "# !apt-get install -y swig cmake ffmpeg freeglut3-dev xvfb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "gWskDE2c9WoN"
   },
   "outputs": [],
   "source": [
    "## uncomment and execute this cell to install all the the dependencies if running in Google Colab or Kaggle\n",
    "\n",
    "# !pip install \"box2d-py==2.3.8\"\n",
    "# !pip install \"stable-baselines3[extra]==2.1.0\"\n",
    "# !pip install \"huggingface_sb3>=3.0\"\n",
    "# !pip install \"moviepy==1.0.3\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FtY8FhliLsGm"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BIedd7Pz9sOs",
    "outputId": "6d080eac-d9ac-4671-8a1c-515fb2ffce2c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-05 12:24:05.942382: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-03-05 12:24:05.947241: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-03-05 12:24:06.116083: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-03-05 12:24:06.116129: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-03-05 12:24:06.116174: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-03-05 12:24:06.137451: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-03-05 12:24:06.138492: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-03-05 12:24:07.782367: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import gymnasium as gym\n",
    "\n",
    "from stable_baselines3.common.env_util import make_atari_env\n",
    "from stable_baselines3.common.vec_env import VecFrameStack\n",
    "from stable_baselines3 import A2C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RapkYvTXL7Cd"
   },
   "source": [
    "## Create the Gym env and instantiate the agent\n",
    "\n",
    "For this example, we will use an environment from Atari Simulator. Atari environments are simulated via the Arcade Learning Environment (ALE) through the [Stella](https://github.com/stella-emu/stella) and the [Arcade Learning Environment](https://github.com/mgbellemare/Arcade-Learning-Environment).\n",
    "\n",
    "![Pong](https://gymnasium.farama.org/_images/pong.gif)\n",
    "\n",
    "\n",
    "We will use [Pong](https://gymnasium.farama.org/environments/atari/pong/) from the Atari game simulator. We will be using a specific variant  `PongNoFrameskip-v4`. For now we will not delve into the details except telling you that in this case the state is the image of the game and actions are the game controller actions. As we will be using images as state, we will use a different variation of policy network called `CnnPolicy` which is based on CNNs (Convolutional Neural Networks).\n",
    "\n",
    "Last notebook we used DQN as the algorithm to train the agent. In this notebook we will be used [A2C algorithm](https://stable-baselines3.readthedocs.io/en/master/modules/a2c.html). A2C is a synchronous, deterministic variant of Asynchronous Advantage Actor Critic ([A3C](https://arxiv.org/abs/1602.01783))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pUWGZp3i9wyf",
    "outputId": "ffbf11c5-fcc4-4b05-840c-ec4f38b52077"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
      "[Powered by Stella]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n"
     ]
    }
   ],
   "source": [
    "# There already exists an environment generator\n",
    "# that will make and wrap atari environments correctly.\n",
    "# Here we are also multi-worker training (n_envs=4 => 4 environments)\n",
    "env_id = \"PongNoFrameskip-v4\"\n",
    "\n",
    "vec_env = make_atari_env(env_id, n_envs=4, seed=0)\n",
    "# Frame-stacking with 4 frames\n",
    "vec_env = VecFrameStack(vec_env, n_stack=4)\n",
    "\n",
    "model = A2C(\"CnnPolicy\", vec_env, verbose=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UQOeDMf7OqmY"
   },
   "source": [
    "## Train the Agent\n",
    "\n",
    "It is advisable to run this on an envirnment with GPU as otherwise it may take long to run this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 640,
     "referenced_widgets": [
      "ca44bf7218914ba58a2a2ef60d01273f",
      "9e37b4d30554454d81cdefd49969b13d"
     ]
    },
    "id": "Gtkged9XOqmZ",
    "outputId": "3bc4fc25-7e53-4a6c-ed4c-1fcbf464b42c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d8f995175534e40b4becf11ccedf3e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------\n",
      "| rollout/              |          |\n",
      "|    ep_len_mean        | 3.72e+03 |\n",
      "|    ep_rew_mean        | -20      |\n",
      "| time/                 |          |\n",
      "|    fps                | 20       |\n",
      "|    iterations         | 500      |\n",
      "|    time_elapsed       | 494      |\n",
      "|    total_timesteps    | 10000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -1.76    |\n",
      "|    explained_variance | 0.0826   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 499      |\n",
      "|    policy_loss        | 0.0543   |\n",
      "|    value_loss         | 0.00113  |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| rollout/              |          |\n",
      "|    ep_len_mean        | 3.39e+03 |\n",
      "|    ep_rew_mean        | -20.5    |\n",
      "| time/                 |          |\n",
      "|    fps                | 28       |\n",
      "|    iterations         | 1000     |\n",
      "|    time_elapsed       | 713      |\n",
      "|    total_timesteps    | 20000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -1.77    |\n",
      "|    explained_variance | 0.0229   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 999      |\n",
      "|    policy_loss        | 0.0452   |\n",
      "|    value_loss         | 0.000777 |\n",
      "------------------------------------\n"
     ]
    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\n"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "<stable_baselines3.a2c.a2c.A2C at 0x7f76656b5490>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you have a GPU, you can increase the `total_timesteps` to something like 1_000_000 i.e. one million\n",
    "# It would take about 45 mins to train\n",
    "# The more you train the better the result will be\n",
    "\n",
    "model.learn(total_timesteps=20_000, log_interval=500, progress_bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cdvmEsyMOqmZ"
   },
   "source": [
    "## Generate a Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uUuhVC_oOqma",
    "outputId": "9cbc4bd6-2ba2-491f-a31d-eca605129eb5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gymnasium/utils/passive_env_checker.py:335: UserWarning: \u001b[33mWARN: No render fps was declared in the environment (env.metadata['render_fps'] is None or not defined), rendering may occur at inconsistent fps.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving video to /home/nsanghi/sandbox/apress/drl-2ed/chapter2/logs/videos/a2c-agent-PongNoFrameskip-v4-step-0-to-step-1000.mp4\n",
      "Moviepy - Building video /home/nsanghi/sandbox/apress/drl-2ed/chapter2/logs/videos/a2c-agent-PongNoFrameskip-v4-step-0-to-step-1000.mp4.\n",
      "Moviepy - Writing video /home/nsanghi/sandbox/apress/drl-2ed/chapter2/logs/videos/a2c-agent-PongNoFrameskip-v4-step-0-to-step-1000.mp4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                        \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moviepy - Done !\n",
      "Moviepy - video ready /home/nsanghi/sandbox/apress/drl-2ed/chapter2/logs/videos/a2c-agent-PongNoFrameskip-v4-step-0-to-step-1000.mp4\n"
     ]
    }
   ],
   "source": [
    "from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv\n",
    "\n",
    "video_length = 1000\n",
    "video_folder = \"logs/videos/\"\n",
    "\n",
    "vec_env = make_atari_env(env_id, n_envs=1, seed=0)\n",
    "# Frame-stacking with 4 frames\n",
    "vec_env = VecFrameStack(vec_env, n_stack=4)\n",
    "\n",
    "obs = vec_env.reset()\n",
    "\n",
    "# Record the video starting at the first step\n",
    "vec_env = VecVideoRecorder(vec_env, video_folder,\n",
    "                       record_video_trigger=lambda x: x == 0, video_length=video_length,\n",
    "                       name_prefix=f\"a2c-agent-{env_id}\")\n",
    "\n",
    "vec_env.reset()\n",
    "for _ in range(video_length + 1):\n",
    "  action, _state = model.predict(obs)\n",
    "  obs, _, _, _ = vec_env.step(action)\n",
    "# Save the video\n",
    "vec_env.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dXKNVnP4Oqma"
   },
   "source": [
    "## Watch the Video\n",
    "\n",
    "To get better results and if you have GPU, increase the `total_timesteps` to something like `1_000_000` in `model.learn` call above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 546
    },
    "id": "K-ZQy2nQOqma",
    "outputId": "56820107-3a9e-4391-92ba-57c20ce14002"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=400 controls>\n",
       "      <source 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type=\"video/mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "from base64 import b64encode\n",
    "mp4 = open('./logs/videos/a2c-agent-PongNoFrameskip-v4-step-0-to-step-1000.mp4','rb').read()\n",
    "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
    "HTML(\"\"\"\n",
    "<video width=400 controls>\n",
    "      <source src=\"%s\" type=\"video/mp4\">\n",
    "</video>\n",
    "\"\"\" % data_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JfLpw1G9Oqmb"
   },
   "source": [
    "## Share the Video on Huggingface Account\n",
    "\n",
    "### First let us login into our huggingface account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
      "9baca00f449c4cbc9aa3191d8b0d619a",
      "72412189f40e48809504332ccb9e2cd8",
      "8fe2b67b410941fcadd5e196f503a8c2",
      "f6739d173b434c17a11f69c21242d2f8",
      "923e2ceb73e441de909349be5599f892",
      "1989d775fe6e4fe8926911f21318ec84",
      "7e247b90e182433b84ee5ce7d209c9b7",
      "d749ca688f144e98871de85ec951ae6c",
      "8cc7aceef2814715b5315f50c54987dd",
      "cc57825b7425411083eaf475725476f0",
      "36582c747a294b9594f66e1d09a1ccd2",
      "2d76f4d10e4e4f2997534fea70d8f21f",
      "59043fffdc984aaa89e377a6a685ce5a",
      "097f1c571f254707801331bdbff7b1f2",
      "4ca564d7a53a471ebb415ca24277ab9d",
      "3d616f8122b643e5b903610a40e93c5b",
      "62df7a429659424c89ab1d75e8d7ea6f",
      "dc5f6de1b5714f8fb8c5d199db345baf",
      "c9146c34bff8416c8c030212ce763aff",
      "389ababb95574d318bf00b90d2a3c4fd",
      "6aaed5c847384ace936493a55044863b",
      "69214bb78261461fba63a21891208026",
      "6b0186d0659745488a1c27f49353df41",
      "90030581155248efb7a52f0967be6b9a",
      "1e6e2c58b1974a3fb60af65cdb430257",
      "7379f8b344a54d6abdd4a2c7c3922bbc",
      "2a903d9f4a704e64b202ffbacf98818d",
      "20bfbd61a95e462d97fca454b9d26164",
      "1de628df6caa4f78bca909f1c1ebb506",
      "dcf4085112d14992bcbd03390c261241",
      "73184ba9020c474b9843ec8feb4e4785",
      "982d0489619242409625f520502d80da"
     ]
    },
    "id": "sUU3FfOXOqmb",
    "outputId": "e71d4c23-0b01-4885-e4b1-4c6a92fde05d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9bc2997c13c94a61a2fba4458f2382bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub\n",
    "from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.\n",
    "\n",
    "notebook_login()\n",
    "!git config --global credential.helper store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT**\n",
    "Some users have reported facing following error while running the `package_to_hub` upload function. \n",
    "\n",
    "```\n",
    "\"Token is required (write-access action) but no token found. You need to provide a token or be logged in to Hugging Face with `huggingface-cli login` or `huggingface_hub.login`. See https://huggingface.co/settings/tokens.\"\n",
    "```\n",
    "\n",
    "In such a case the following command will help you over come the issue\n",
    "\n",
    "```\n",
    "import huggingface_hub\n",
    "\n",
    "huggingface_hub.login(token= <YOUR_HF_TOKEN>,\n",
    "                     write_permission = True,\n",
    "                    add_to_git_credential = True)\n",
    "\t\t\t\t\t\n",
    "```\n",
    "\n",
    "Another alternative is to use following command from command shell where the `venv` or `conda` environment for this repository has been activated and then follow the instructions to set the HuggingFace token.\n",
    "\n",
    "```\n",
    "huggingface-cli login\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i84HLr3lOqmb"
   },
   "source": [
    "### Push to HuggingFace\n",
    "\n",
    "You can execute the code below to push the trained agent to huggingface hub. Towards the end of output there will be a link of the hosted model that you can share with your friends and family. Share the url without the ending paths \"tree/main\" so that the url takes them to Model Card tab where they can see the animation. e.g. in my case it will be:<br/>\n",
    "`https://huggingface.co/nsanghi/a2c-Atari-Pong/`<br/>\n",
    "instead of<br/>\n",
    "`https://huggingface.co/nsanghi/a2c-Atari-Pong/tree/main/`\n",
    "\n",
    "NOTE: At the time of writing this book, there is a bug in push_to_hub which results in sample video not getting created. You can refer to this bug here - https://github.com/huggingface/huggingface_sb3/issues/33\n",
    "\n"
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     "text": [
      "Moviepy - Building video /home/nsanghi/sandbox/apress/drl-2ed/chapter2/logs/videos/a2c-agent-PongNoFrameskip-v4-step-1001-to-step-2001.mp4.\n",
      "Moviepy - Writing video /home/nsanghi/sandbox/apress/drl-2ed/chapter2/logs/videos/a2c-agent-PongNoFrameskip-v4-step-1001-to-step-2001.mp4\n",
      "\n"
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      "Moviepy - Done !\n",
      "Moviepy - video ready /home/nsanghi/sandbox/apress/drl-2ed/chapter2/logs/videos/a2c-agent-PongNoFrameskip-v4-step-1001-to-step-2001.mp4\n",
      "\u001b[38;5;4mℹ This function will save, evaluate, generate a video of your agent,\n",
      "create a model card and push everything to the hub. It might take up to 1min.\n",
      "This is a work in progress: if you encounter a bug, please open an issue.\u001b[0m\n"
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      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gymnasium/utils/passive_env_checker.py:335: UserWarning: \u001b[33mWARN: No render fps was declared in the environment (env.metadata['render_fps'] is None or not defined), rendering may occur at inconsistent fps.\u001b[0m\n",
      "  logger.warn(\n"
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      "Saving video to /tmp/tmp2rall_56/-step-0-to-step-1000.mp4\n",
      "Moviepy - Building video /tmp/tmp2rall_56/-step-0-to-step-1000.mp4.\n",
      "Moviepy - Writing video /tmp/tmp2rall_56/-step-0-to-step-1000.mp4\n",
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     "text": [
      "\u001b[38;5;4mℹ Pushing repo nsanghi/a2c-Atari-Pong to the Hugging Face Hub\u001b[0m\n"
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      "\u001b[38;5;4mℹ Your model is pushed to the Hub. You can view your model here:\n",
      "https://huggingface.co/nsanghi/a2c-Atari-Pong/tree/main/\u001b[0m\n"
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    "eval_env = make_atari_env(env_id, n_envs=1, seed=0)\n",
    "# Frame-stacking with 4 frames\n",
    "eval_env = VecFrameStack(eval_env, n_stack=4)\n",
    "\n",
    "obs = vec_env.reset()\n",
    "\n",
    "# method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub\n",
    "\n",
    "# Please note repo_id is of the form <huggingface_id>/<name of repo>\n",
    "# you will need to change this to \"<your_huggingface_id>/dqn-LunarLander-v2\"\n",
    "\n",
    "package_to_hub(model=model, # Our trained model\n",
    "               model_name=\"A2C-Atari-Pong\", # The name of our trained model\n",
    "               model_architecture=\"A2C\", # The model architecture we used: in our case PPO\n",
    "               env_id=env_id, # Name of the environment\n",
    "               eval_env=eval_env, # Evaluation Environment\n",
    "               repo_id=\"nsanghi/a2c-Atari-Pong\", # id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name}\n",
    "               commit_message=\"Push to Hub\")"
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